Retinal Distance Screening With Machine Learning for Ocular Abnormalities
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Solution Overview
Problem
Existing retinal distance-screening technologies face inefficiencies due to varying referral standards for face-to-face exams, leading to potential reductions in the standard of care, particularly for mild nonproliferative diabetic retinopathy, and fail to accurately detect co-existing ocular abnormalities.
Innovation Solution
Implementing machine learning models for retinal image analysis, including locus, fitness, and detection models, to enhance the detection of ocular abnormalities, using transfer learning techniques and dataset partitioning to ensure model generalizability and accuracy, with integration into telehealth systems for efficient screening.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are implemented for retinal image analysis, then detection accuracy of ocular abnormalities is improved, but device complexity increases
Solution Approach 1:
The patent introduces a cloud-based processing system as an intermediary between the mobile imaging device and the analysis models. The mobile device captures images and transmits them to the cloud, where ML models perform analysis. This intermediary approach allows complex ML processing without increasing on-device complexity, resolving the contradiction between detection accuracy and device complexity.
Solution Approach 2:
The patent replaces manual visual inspection by eye care professionals with automated machine learning models. The ML models process retinal images through computational algorithms, substituting the mechanical/visual process with an automated digital processing system. This enables high detection accuracy while keeping the end-user interface simple.
2Reliability
If referral standards are strictly enforced, then standard of care is improved, but number of referrals increases
Solution Approach 1:
The patent implements a feedback mechanism where ML models continuously learn from annotated images and refine their detection algorithms. The system provides feedback to both the screening process and the models themselves through iterative training on new data. This feedback loop enables the system to maintain high detection accuracy while reducing unnecessary referrals by learning from past performance and adjusting criteria.
Solution Approach 2:
The patent performs preliminary screening using ML models before final referral decisions are made. The models conduct an initial assessment of retinal images and flag only the most critical cases for human review and referral. This preliminary action filters out unnecessary referrals while ensuring that all serious cases are captured, maintaining standard of care without excessive referral volume.
3Adaptability or versatility
If multiple ocular abnormalities are detected, then comprehensiveness of screening is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent segments the detection task into separate specialized ML models, each trained to detect specific ocular abnormalities such as diabetic retinopathy, glaucoma, and macular degeneration. Each model focuses on one or a few abnormality types, making the detection process more manageable. The segmented approach enables comprehensive multi-condition screening while reducing the complexity of detecting and measuring each individual abnormality type.
Data Source
AI summary
Methods, systems, and apparatuses are provided for retinal distance-screening using machine learning.